Covid 19 Analysis

By: Suraj Honkamble.

Import required libraries

import pandas as pd
pd.options.display.max_columns=50
import numpy as np
import seaborn as sns
sns.set_style('darkgrid')
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
import plotly.express as px
import plotly.graph_objects as go

Load and read the dataset

df=pd.read_csv('D:\\DATA SCIENCE Internship with CodersCave\\Data\\Covid Data.csv')
df.head()

	USMER	MEDICAL_UNIT	SEX	PATIENT_TYPE	DATE_DIED	INTUBED	PNEUMONIA	AGE	PREGNANT	DIABETES	COPD	ASTHMA	INMSUPR	HIPERTENSION	OTHER_DISEASE	CARDIOVASCULAR	OBESITY	RENAL_CHRONIC	TOBACCO	CLASIFFICATION_FINAL	ICU
0	2	1	1	1	03/05/2020	97	1	65	2	2	2	2	2	1	2	2	2	2	2	3	97
1	2	1	2	1	03/06/2020	97	1	72	97	2	2	2	2	1	2	2	1	1	2	5	97
2	2	1	2	2	09/06/2020	1	2	55	97	1	2	2	2	2	2	2	2	2	2	3	2
3	2	1	1	1	12/06/2020	97	2	53	2	2	2	2	2	2	2	2	2	2	2	7	97
4	2	1	2	1	21/06/2020	97	2	68	97	1	2	2	2	1	2	2	2	2	2	3	97

Column information

In the Boolean features, 1 means "yes" and 2 means "no" and values as 97 and 99 are missing data.

• USMER -Indicates whether the patient treated medical units of the first, second or third level.
• MEDICAL_UNIT- Type of institution of the National Health System that provided the care.
• SEX - 1-female. 2-male
• PATIENT_TYPE - Type of care the patient received in the unit. 1 for returned home and 2 for hospitalization.
• DATE_DIED - If the patient died indicate the date of death, and 9999-99-99 otherwise.
• INTUBED - Whether the patient was connected to the ventilator.
• PNEUMONIA - Whether the patient already have air sacs inflammation or not.
• AGE - Age of the patient.
• PREGNANT - Whether the patient is pregnant or not.
• DIABETES - Whether the patient has diabetes or not
• COPD - Whether the patient has Chronic obstructive pulmonary disease or not
• ASTHMA - Whether the patient has asthma or not
• INMSUPR - Whether the patient is immunosuppressed or not.
• HIPERTENSION - Whether the patient has hypertension or not
• OTHER_DISEASE - Whether the patient has other disease or not
• CARDIOVASCULAR - Whether the patient has heart or blood vessels related disease
• OBESITY- Whether the patient is obese or not
• RENAL_CHRONIC - Whether the patient has chronic renal disease or not
• TOBACCO - Whether the patient is a tobacco user
• CLASIFFICATION_FINAL - Covid test results. covid test findings. Values 1-3 mean that the patient was diagnosed with covid in different degrees. 4 or higher means that the patient is not a carrier of covid or that the test is inconclusive.
• ICU - Whether the patient had been admitted to an Intensive Care Unit

df.shape

(1048575, 21)

df.columns

Index(['USMER', 'MEDICAL_UNIT', 'SEX', 'PATIENT_TYPE', 'DATE_DIED', 'INTUBED',
       'PNEUMONIA', 'AGE', 'PREGNANT', 'DIABETES', 'COPD', 'ASTHMA', 'INMSUPR',
       'HIPERTENSION', 'OTHER_DISEASE', 'CARDIOVASCULAR', 'OBESITY',
       'RENAL_CHRONIC', 'TOBACCO', 'CLASIFFICATION_FINAL', 'ICU'],
      dtype='object')

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1048575 entries, 0 to 1048574
Data columns (total 21 columns):
 #   Column                Non-Null Count    Dtype 
---  ------                --------------    ----- 
 0   USMER                 1048575 non-null  int64 
 1   MEDICAL_UNIT          1048575 non-null  int64 
 2   SEX                   1048575 non-null  int64 
 3   PATIENT_TYPE          1048575 non-null  int64 
 4   DATE_DIED             1048575 non-null  object
 5   INTUBED               1048575 non-null  int64 
 6   PNEUMONIA             1048575 non-null  int64 
 7   AGE                   1048575 non-null  int64 
 8   PREGNANT              1048575 non-null  int64 
 9   DIABETES              1048575 non-null  int64 
 10  COPD                  1048575 non-null  int64 
 11  ASTHMA                1048575 non-null  int64 
 12  INMSUPR               1048575 non-null  int64 
 13  HIPERTENSION          1048575 non-null  int64 
 14  OTHER_DISEASE         1048575 non-null  int64 
 15  CARDIOVASCULAR        1048575 non-null  int64 
 16  OBESITY               1048575 non-null  int64 
 17  RENAL_CHRONIC         1048575 non-null  int64 
 18  TOBACCO               1048575 non-null  int64 
 19  CLASIFFICATION_FINAL  1048575 non-null  int64 
 20  ICU                   1048575 non-null  int64 
dtypes: int64(20), object(1)
memory usage: 168.0+ MB

df.isna().sum()

USMER                   0
MEDICAL_UNIT            0
SEX                     0
PATIENT_TYPE            0
DATE_DIED               0
INTUBED                 0
PNEUMONIA               0
AGE                     0
PREGNANT                0
DIABETES                0
COPD                    0
ASTHMA                  0
INMSUPR                 0
HIPERTENSION            0
OTHER_DISEASE           0
CARDIOVASCULAR          0
OBESITY                 0
RENAL_CHRONIC           0
TOBACCO                 0
CLASIFFICATION_FINAL    0
ICU                     0
dtype: int64

df.nunique()

USMER                     2
MEDICAL_UNIT             13
SEX                       2
PATIENT_TYPE              2
DATE_DIED               401
INTUBED                   4
PNEUMONIA                 3
AGE                     121
PREGNANT                  4
DIABETES                  3
COPD                      3
ASTHMA                    3
INMSUPR                   3
HIPERTENSION              3
OTHER_DISEASE             3
CARDIOVASCULAR            3
OBESITY                   3
RENAL_CHRONIC             3
TOBACCO                   3
CLASIFFICATION_FINAL      7
ICU                       4
dtype: int64

Data Cleaning

Unique values in all categorical column

cat=['USMER', 'MEDICAL_UNIT', 'SEX', 'PATIENT_TYPE', 'INTUBED',
       'PNEUMONIA', 'PREGNANT', 'DIABETES', 'COPD', 'ASTHMA', 'INMSUPR',
       'HIPERTENSION', 'OTHER_DISEASE', 'CARDIOVASCULAR', 'OBESITY',
       'RENAL_CHRONIC', 'TOBACCO', 'CLASIFFICATION_FINAL', 'ICU']

for i in cat:
    print("Column Name : {}\nTotal unique values: {}\nUnique values: {}".format(i, df[i].nunique(), df[i].unique()))
    print("**--**"*10)

Column Name : USMER
Total unique values: 2
Unique values: [2 1]
**--****--****--****--****--****--****--****--****--****--**
Column Name : MEDICAL_UNIT
Total unique values: 13
Unique values: [ 1  2  3  4  5  6  7  8  9 10 11 12 13]
**--****--****--****--****--****--****--****--****--****--**
Column Name : SEX
Total unique values: 2
Unique values: [1 2]
**--****--****--****--****--****--****--****--****--****--**
Column Name : PATIENT_TYPE
Total unique values: 2
Unique values: [1 2]
**--****--****--****--****--****--****--****--****--****--**
Column Name : INTUBED
Total unique values: 4
Unique values: [97  1  2 99]
**--****--****--****--****--****--****--****--****--****--**
Column Name : PNEUMONIA
Total unique values: 3
Unique values: [ 1  2 99]
**--****--****--****--****--****--****--****--****--****--**
Column Name : PREGNANT
Total unique values: 4
Unique values: [ 2 97 98  1]
**--****--****--****--****--****--****--****--****--****--**
Column Name : DIABETES
Total unique values: 3
Unique values: [ 2  1 98]
**--****--****--****--****--****--****--****--****--****--**
Column Name : COPD
Total unique values: 3
Unique values: [ 2  1 98]
**--****--****--****--****--****--****--****--****--****--**
Column Name : ASTHMA
Total unique values: 3
Unique values: [ 2  1 98]
**--****--****--****--****--****--****--****--****--****--**
Column Name : INMSUPR
Total unique values: 3
Unique values: [ 2  1 98]
**--****--****--****--****--****--****--****--****--****--**
Column Name : HIPERTENSION
Total unique values: 3
Unique values: [ 1  2 98]
**--****--****--****--****--****--****--****--****--****--**
Column Name : OTHER_DISEASE
Total unique values: 3
Unique values: [ 2  1 98]
**--****--****--****--****--****--****--****--****--****--**
Column Name : CARDIOVASCULAR
Total unique values: 3
Unique values: [ 2  1 98]
**--****--****--****--****--****--****--****--****--****--**
Column Name : OBESITY
Total unique values: 3
Unique values: [ 2  1 98]
**--****--****--****--****--****--****--****--****--****--**
Column Name : RENAL_CHRONIC
Total unique values: 3
Unique values: [ 2  1 98]
**--****--****--****--****--****--****--****--****--****--**
Column Name : TOBACCO
Total unique values: 3
Unique values: [ 2  1 98]
**--****--****--****--****--****--****--****--****--****--**
Column Name : CLASIFFICATION_FINAL
Total unique values: 7
Unique values: [3 5 7 6 1 2 4]
**--****--****--****--****--****--****--****--****--****--**
Column Name : ICU
Total unique values: 4
Unique values: [97  2  1 99]
**--****--****--****--****--****--****--****--****--****--**

In the Binary categorical features like Pneumonia, Diabetis etc we can see three or four unique values. As mentioned in dataset that values 97,98, 99 are corresponds to missing values. Lets replace 97,98, 99 with np.nan.

multi=['INTUBED', 'PNEUMONIA', 'PREGNANT', 'DIABETES', 'COPD', 'ASTHMA', 'INMSUPR',
       'HIPERTENSION', 'OTHER_DISEASE', 'CARDIOVASCULAR', 'OBESITY',
       'RENAL_CHRONIC', 'TOBACCO', 'ICU']

Above Columns contains 97,98 and 99 values lets replcae them

for i in multi:
    df[i].replace({99:np.nan, 97:np.nan, 98:np.nan}, inplace=True)
    print("Column Name : {}\nTotal unique values: {}\nUnique values: {}".format(i, df[i].nunique(), df[i].unique()))
    print("**--**"*10)

Column Name : INTUBED
Total unique values: 2
Unique values: [nan  1.  2.]
**--****--****--****--****--****--****--****--****--****--**
Column Name : PNEUMONIA
Total unique values: 2
Unique values: [ 1.  2. nan]
**--****--****--****--****--****--****--****--****--****--**
Column Name : PREGNANT
Total unique values: 2
Unique values: [ 2. nan  1.]
**--****--****--****--****--****--****--****--****--****--**
Column Name : DIABETES
Total unique values: 2
Unique values: [ 2.  1. nan]
**--****--****--****--****--****--****--****--****--****--**
Column Name : COPD
Total unique values: 2
Unique values: [ 2.  1. nan]
**--****--****--****--****--****--****--****--****--****--**
Column Name : ASTHMA
Total unique values: 2
Unique values: [ 2.  1. nan]
**--****--****--****--****--****--****--****--****--****--**
Column Name : INMSUPR
Total unique values: 2
Unique values: [ 2.  1. nan]
**--****--****--****--****--****--****--****--****--****--**
Column Name : HIPERTENSION
Total unique values: 2
Unique values: [ 1.  2. nan]
**--****--****--****--****--****--****--****--****--****--**
Column Name : OTHER_DISEASE
Total unique values: 2
Unique values: [ 2.  1. nan]
**--****--****--****--****--****--****--****--****--****--**
Column Name : CARDIOVASCULAR
Total unique values: 2
Unique values: [ 2.  1. nan]
**--****--****--****--****--****--****--****--****--****--**
Column Name : OBESITY
Total unique values: 2
Unique values: [ 2.  1. nan]
**--****--****--****--****--****--****--****--****--****--**
Column Name : RENAL_CHRONIC
Total unique values: 2
Unique values: [ 2.  1. nan]
**--****--****--****--****--****--****--****--****--****--**
Column Name : TOBACCO
Total unique values: 2
Unique values: [ 2.  1. nan]
**--****--****--****--****--****--****--****--****--****--**
Column Name : ICU
Total unique values: 2
Unique values: [nan  2.  1.]
**--****--****--****--****--****--****--****--****--****--**

Now check the missing values

df.isna().sum()

USMER                        0
MEDICAL_UNIT                 0
SEX                          0
PATIENT_TYPE                 0
DATE_DIED                    0
INTUBED                 855869
PNEUMONIA                16003
AGE                          0
PREGNANT                527265
DIABETES                  3338
COPD                      3003
ASTHMA                    2979
INMSUPR                   3404
HIPERTENSION              3104
OTHER_DISEASE             5045
CARDIOVASCULAR            3076
OBESITY                   3032
RENAL_CHRONIC             3006
TOBACCO                   3220
CLASIFFICATION_FINAL         0
ICU                     856032
dtype: int64

CLASIFFICATION_FINAL - Covid test results. covid test findings. Values 1-3 mean that the patient was diagnosed with covid in different degrees. 4 or higher means that the patient is not a carrier of covid or that the test is inconclusive.

We can replcae these values with 1 and 2, Values from 1 to 3 as 1(Positive) and 4 to 7 as 2(Negative)

def test_result(result):
    if (result==1 or result==3 or result==3):
        return 1
    else:
        return 2

df['CLASIFFICATION_FINAL']=df['CLASIFFICATION_FINAL'].apply(test_result)
df['CLASIFFICATION_FINAL'].unique()

array([1, 2], dtype=int64)

Duplicate rows

df.duplicated().sum()

837028

We are just going to analyse the data so I am not going to drop any duplicates.

In the DIED_DATE column there is one entry "9999-99-99" which indicates person not died. Using this column create a new column "Died", if the DIED_DATE is "9999-99-99" then return 2(Not Died) or else return 1(Died).

def died(dod):
    if dod=='9999-99-99':
        return 2
    else:
        return 1
df['DEAD'] = df['DATE_DIED'].apply(died)
df['DEAD'].unique()

array([1, 2], dtype=int64)

Replace "9999-99-99" this died date with 0 so that we can analyze it properly.

df['DATE_DIED'].replace({'9999-99-99':np.nan}, inplace=True)

Change data type to datetime

df['DATE_DIED']=pd.to_datetime(df['DATE_DIED'])

df.dtypes

USMER                            int64
MEDICAL_UNIT                     int64
SEX                              int64
PATIENT_TYPE                     int64
DATE_DIED               datetime64[ns]
INTUBED                        float64
PNEUMONIA                      float64
AGE                              int64
PREGNANT                       float64
DIABETES                       float64
COPD                           float64
ASTHMA                         float64
INMSUPR                        float64
HIPERTENSION                   float64
OTHER_DISEASE                  float64
CARDIOVASCULAR                 float64
OBESITY                        float64
RENAL_CHRONIC                  float64
TOBACCO                        float64
CLASIFFICATION_FINAL             int64
ICU                            float64
DEAD                             int64
dtype: object

Data Analysis

Distribution of Age

plt.figure(figsize=(12,5))
sns.kdeplot(df['AGE'], shade=True, shade_lowest=False)
plt.title("Distribution of Patients Age", fontsize=18, color='green');

Count of Categorical column

df.hist(figsize=(20,20));

Correlation Matrix

plt.figure(figsize=(20,10))
sns.heatmap(df.corr(), annot=True, cmap='Blues');

Convert all the Descrete categorical columns into object

for i in df.columns:
    if (i!='AGE' and i!='DATE_DIED'):
        df[i]=df[i].astype('O')
    else:
        pass

df.dtypes

USMER                           object
MEDICAL_UNIT                    object
SEX                             object
PATIENT_TYPE                    object
DATE_DIED               datetime64[ns]
INTUBED                         object
PNEUMONIA                       object
AGE                              int64
PREGNANT                        object
DIABETES                        object
COPD                            object
ASTHMA                          object
INMSUPR                         object
HIPERTENSION                    object
OTHER_DISEASE                   object
CARDIOVASCULAR                  object
OBESITY                         object
RENAL_CHRONIC                   object
TOBACCO                         object
CLASIFFICATION_FINAL            object
ICU                             object
DEAD                            object
dtype: object

Rename the values are colmns so that it is easy to understand for everyone.

df['USMER'].replace({1:"Primary Treatment", 2:"No Primary Treatment"}, inplace=True)
df['SEX'].replace({1:"Female", 2:"Male"}, inplace=True)
df['PATIENT_TYPE'].replace({1:"Sent Home", 2:"Hospitalized"}, inplace=True)
df['INTUBED'].replace({1:"On Ventilator", 2:"Not On Ventilator"}, inplace=True)
df['PNEUMONIA'].replace({1:"Pneumonia Patient", 2:"Not Pneumonia Patient"}, inplace=True)
df['PREGNANT'].replace({1:"Pregnent", 2:"Not Pregnent"}, inplace=True)
df['DIABETES'].replace({1:"Diabetic Patient", 2:"Non Diabetic Patient"}, inplace=True)
df['COPD'].replace({1:"COP Patient", 2:"Non COP Patient"}, inplace=True)
df['ASTHMA'].replace({1:"Asthama Patient", 2:"Non Asthama Patient"}, inplace=True)
df['INMSUPR'].replace({1:"Weak Immune System", 2:"Strong Immune System"}, inplace=True)
df['HIPERTENSION'].replace({1:"Hipertension Patient", 2:"Non Hipertension Patience"}, inplace=True)
df['OTHER_DISEASE'].replace({1:"Other Diseases", 2:"No Other Diseases"}, inplace=True)
df['CARDIOVASCULAR'].replace({1:"Heart Patient", 2:"Not Heart Patient"}, inplace=True)
df['OBESITY'].replace({1:"Obese", 2:"Not Obese"}, inplace=True)
df['RENAL_CHRONIC'].replace({1:"Chronic", 2:"Not Chronic"}, inplace=True)
df['TOBACCO'].replace({1:"Consume Tobacco", 2:"Not Consume Tobacco"}, inplace=True)
df['CLASIFFICATION_FINAL'].replace({1:"Infected", 2:"Non-Inffected"}, inplace=True)
df['ICU'].replace({1:"In ICU", 2:"Not in ICU"}, inplace=True)
df['DEAD'].replace({1:"Died", 2:"Not Died"}, inplace=True)

Age vs Covid Results(Classification FInal)

plt.figure(figsize=(12,4))
sns.boxplot(x='CLASIFFICATION_FINAL', y='AGE', data=df)
plt.title("Age vs Covid Results", fontsize=18, color='green');

Any Age people are getting infected.

After taking primary level treatment how many patients died and survived?

patient=pd.crosstab(index=df['USMER'], columns=df['DEAD'])
patient.reset_index(inplace=True)
patient

DEAD	USMER	Died	Not Died
0	No Primary Treatment	33787	629116
1	Primary Treatment	43155	342517

fig=px.bar(data_frame=patient, x='USMER', y=['Died'], text_auto=True, color='USMER',
          title="Count of Patients who died after Taking Primary Treatment" )
fig.show()

43k People died after taking the primary treatment and 33k People died with no primary treatment.

In which level of medical Institution more patience are died?

patient=pd.crosstab(index=df['MEDICAL_UNIT'], columns=df['DEAD'])
patient.reset_index(inplace=True)
patient

DEAD	MEDICAL_UNIT	Died	Not Died
0	1	5	146
1	2	5	164
2	3	1492	17683
3	4	39905	274500
4	5	607	6637
5	6	5790	34794
6	7	40	851
7	8	1171	9228
8	9	1369	36747
9	10	1468	6405
10	11	409	5168
11	12	24620	578375
12	13	61	935

fig=px.bar(data_frame=patient, x='MEDICAL_UNIT', y='Died', text_auto=True, color='MEDICAL_UNIT',
          title="Count of Died Patients In Institution Level" )
fig.show()

More Patients died in Medical Unit=4 then in Medical Unit=12

Who's chances are more getting inffected, Male or Female?

inff_patient=pd.crosstab(index=df['SEX'], columns=df['CLASIFFICATION_FINAL'])
inff_patient.reset_index(inplace=True)
inff_patient

CLASIFFICATION_FINAL	SEX	Infected	Non-Inffected
0	Female	181830	343234
1	Male	208298	315213

Count of Patients died by Gender

died_patient=pd.crosstab(index=df['SEX'], columns=df['DEAD'])
died_patient.reset_index(inplace=True)
died_patient

DEAD	SEX	Died	Not Died
0	Female	27402	497662
1	Male	49540	473971

Cout of patients who got inffected and died

patients=pd.merge(inff_patient, died_patient, on='SEX', how='inner')
patients

	SEX	Infected	Non-Inffected	Died	Not Died
0	Female	181830	343234	27402	497662
1	Male	208298	315213	49540	473971

fig=px.bar(data_frame=patients, x='SEX', y=['Infected','Died'], text_auto=True,
          title="Gender wise Count of Inffected & Died Patients" )
fig.show()

Male and Female both are getting Inffected with same rate.

Male Deaths are more than the Female.

What type of patients are died?

patient=pd.crosstab(index=df['PATIENT_TYPE'], columns=df['DEAD'])
patient.reset_index(inplace=True)
patient

DEAD	PATIENT_TYPE	Died	Not Died
0	Hospitalized	70066	129965
1	Sent Home	6876	841668

fig=px.bar(data_frame=patient, x='PATIENT_TYPE', y=['Died','Not Died'], text_auto=True,
          title="Count of Patients died by Type" )
fig.show()

Most of the Hospitalized people died.

How many Patients are died after keeping them on Ventilator?

patient=pd.crosstab(index=df['INTUBED'], columns=df['DEAD'])
patient.reset_index(inplace=True)
patient

DEAD	INTUBED	Died	Not Died
0	Not On Ventilator	41601	117449
1	On Ventilator	26381	7275

fig=px.bar(data_frame=patient, x='INTUBED', y=['Died','Not Died'], text_auto=True,
          title="Count of Patients died after keeping them on Ventilator" )
fig.show()

Many Patients died without availability of Ventilators. And when the patient went on Ventilators the chances of death is high.

How Many Pneumonia Patients got inffected by Corona Virus?

inff_patient=pd.crosstab(index=df['PNEUMONIA'], columns=df['CLASIFFICATION_FINAL'])
inff_patient.reset_index(inplace=True)
inff_patient

CLASIFFICATION_FINAL	PNEUMONIA	Infected	Non-Inffected
0	Not Pneumonia Patient	305211	587323
1	Pneumonia Patient	84913	55125

How many Pneumonia Patients Died Due to Corona Virus?

died_patient=pd.crosstab(index=df['PNEUMONIA'], columns=df['DEAD'])
died_patient.reset_index(inplace=True)
died_patient

DEAD	PNEUMONIA	Died	Not Died
0	Not Pneumonia Patient	22285	870249
1	Pneumonia Patient	53923	86115

Cout of Inffected and died Pneumonia patient

patients=pd.merge(inff_patient, died_patient, on='PNEUMONIA', how='inner')
patients

	PNEUMONIA	Infected	Non-Inffected	Died	Not Died
0	Not Pneumonia Patient	305211	587323	22285	870249
1	Pneumonia Patient	84913	55125	53923	86115

fig=px.bar(data_frame=patients, x='PNEUMONIA', y=['Infected','Died'], text_auto=True,
          title="Count of Pneumonia Patients got inffected and died by Corona Virus" )
fig.show()

About 70% Corona Inffected Pneumonia Patients died.

How many Pregnent WOmes got inffected?

inff_patient=pd.crosstab(index=df['PREGNANT'], columns=df['CLASIFFICATION_FINAL'])
inff_patient.reset_index(inplace=True)
inff_patient

CLASIFFICATION_FINAL	PREGNANT	Infected	Non-Inffected
0	Not Pregnent	177693	335486
1	Pregnent	2754	5377

How Many Pregnent Inffected women died?

died_patient=pd.crosstab(index=df['PREGNANT'], columns=df['DEAD'])
died_patient.reset_index(inplace=True)
died_patient

DEAD	PREGNANT	Died	Not Died
0	Not Pregnent	27246	485933
1	Pregnent	89	8042

patients=pd.merge(inff_patient, died_patient, on='PREGNANT', how='inner')
patients

	PREGNANT	Infected	Non-Inffected	Died	Not Died
0	Not Pregnent	177693	335486	27246	485933
1	Pregnent	2754	5377	89	8042

fig=px.bar(data_frame=patients, x='PREGNANT', y=['Infected','Died'], text_auto=True,
          title="Count of Inffected & Death Patients by Pregnency" )
fig.show()

Very less paregnent women got inffected and the count of death is also very low.

How many Diabetic patients got inffected?

inff_patient=pd.crosstab(index=df['DIABETES'], columns=df['CLASIFFICATION_FINAL'])
inff_patient.reset_index(inplace=True)
inff_patient

CLASIFFICATION_FINAL	DIABETES	Infected	Non-Inffected
0	Diabetic Patient	61445	63544
1	Non Diabetic Patient	327276	592972

Count of dibetic patients died due to covid

died_patient=pd.crosstab(index=df['DIABETES'], columns=df['DEAD'])
died_patient.reset_index(inplace=True)
died_patient

DEAD	DIABETES	Died	Not Died
0	Diabetic Patient	28265	96724
1	Non Diabetic Patient	47946	872302

Count of Diabetic inffected and died Patient

patients=pd.merge(inff_patient, died_patient, on='DIABETES', how='inner')
patients

	DIABETES	Infected	Non-Inffected	Died	Not Died
0	Diabetic Patient	61445	63544	28265	96724
1	Non Diabetic Patient	327276	592972	47946	872302

fig=px.bar(data_frame=patients, x='DIABETES', y=['Infected','Died'], text_auto=True,
          title="Gender wise Count of Inffected Patients" )
fig.show()

About 40% Diabetic Patients died after getting inffected by Corona VIrus.

How many COP Disease people got inffected?

inff_patient=pd.crosstab(index=df['COPD'], columns=df['CLASIFFICATION_FINAL'])
inff_patient.reset_index(inplace=True)
inff_patient

CLASIFFICATION_FINAL	COPD	Infected	Non-Inffected
0	COP Patient	6048	9014
1	Non COP Patient	382806	647704

Count of COP disease people died due to Corona

died_patient=pd.crosstab(index=df['COPD'], columns=df['DEAD'])
died_patient.reset_index(inplace=True)
died_patient

DEAD	COPD	Died	Not Died
0	COP Patient	4021	11041
1	Non COP Patient	72210	958300

Count of COP Disease patient inffected and died

patients=pd.merge(inff_patient, died_patient, on='COPD', how='inner')
patients

	COPD	Infected	Non-Inffected	Died	Not Died
0	COP Patient	6048	9014	4021	11041
1	Non COP Patient	382806	647704	72210	958300

fig=px.bar(data_frame=patients, x='COPD', y=['Infected','Died'], text_auto=True,
          title="Count COP Disease people getting inffected and died" )
fig.show()

Death rate for COP disease people after getting covid inffection is very high.almost 70% Patients died.

How Many Asthma patient got inffected?

inff_patient=pd.crosstab(index=df['ASTHMA'], columns=df['CLASIFFICATION_FINAL'])
inff_patient.reset_index(inplace=True)
inff_patient

CLASIFFICATION_FINAL	ASTHMA	Infected	Non-Inffected
0	Asthama Patient	10382	21190
1	Non Asthama Patient	378478	635546

Count of Asthama people died due to corona

died_patient=pd.crosstab(index=df['ASTHMA'], columns=df['DEAD'])
died_patient.reset_index(inplace=True)
died_patient

DEAD	ASTHMA	Died	Not Died
0	Asthama Patient	1480	30092
1	Non Asthama Patient	74758	939266

Count of Inffected and died Asthma patients

patients=pd.merge(inff_patient, died_patient, on='ASTHMA', how='inner')
patients

	ASTHMA	Infected	Non-Inffected	Died	Not Died
0	Asthama Patient	10382	21190	1480	30092
1	Non Asthama Patient	378478	635546	74758	939266

fig=px.bar(data_frame=patients, x='ASTHMA', y=['Infected','Died'], text_auto=True,
          title="Count of Inffected and died Asthma patients" )
fig.show()

10% Asthma Patient died after covid inffection.

How many people with low Immune Systemgot inffected?

inff_patient=pd.crosstab(index=df['INMSUPR'], columns=df['CLASIFFICATION_FINAL'])
inff_patient.reset_index(inplace=True)
inff_patient

CLASIFFICATION_FINAL	INMSUPR	Infected	Non-Inffected
0	Strong Immune System	383990	647011
1	Weak Immune System	4726	9444

Count of people with weak Immune system died due to covid

died_patient=pd.crosstab(index=df['INMSUPR'], columns=df['DEAD'])
died_patient.reset_index(inplace=True)
died_patient

DEAD	INMSUPR	Died	Not Died
0	Strong Immune System	73569	957432
1	Weak Immune System	2618	11552

Count of Low immune system people getting inffected and died

patients=pd.merge(inff_patient, died_patient, on='INMSUPR', how='inner')
patients

	INMSUPR	Infected	Non-Inffected	Died	Not Died
0	Strong Immune System	383990	647011	73569	957432
1	Weak Immune System	4726	9444	2618	11552

fig=px.bar(data_frame=patients, x='INMSUPR', y=['Infected','Died'], text_auto=True,
          title="people with low Immune System inffected and died" )
fig.show()

55% Low immune system people died afted covid affection.

How many Hypertensed people got inffected

inff_patient=pd.crosstab(index=df['HIPERTENSION'], columns=df['CLASIFFICATION_FINAL'])
inff_patient.reset_index(inplace=True)
inff_patient

CLASIFFICATION_FINAL	HIPERTENSION	Infected	Non-Inffected
0	Hipertension Patient	75946	86783
1	Non Hipertension Patience	312833	569909

How many Hypertensed people died

died_patient=pd.crosstab(index=df['HIPERTENSION'], columns=df['DEAD'])
died_patient.reset_index(inplace=True)
died_patient

DEAD	HIPERTENSION	Died	Not Died
0	Hipertension Patient	32061	130668
1	Non Hipertension Patience	44191	838551

Count of Hypertensed people died after corona inffection

patients=pd.merge(inff_patient, died_patient, on='HIPERTENSION', how='inner')
patients

	HIPERTENSION	Infected	Non-Inffected	Died	Not Died
0	Hipertension Patient	75946	86783	32061	130668
1	Non Hipertension Patience	312833	569909	44191	838551

fig=px.bar(data_frame=patients, x='HIPERTENSION', y=['Infected','Died'], text_auto=True,
          title="Count of Hypertensed people died after corona inffection" )
fig.show()

About 40% Inffected Hypertension people died.

How many with Heart related diseases got covid inffected

inff_patient=pd.crosstab(index=df['CARDIOVASCULAR'], columns=df['CLASIFFICATION_FINAL'])
inff_patient.reset_index(inplace=True)
inff_patient

CLASIFFICATION_FINAL	CARDIOVASCULAR	Infected	Non-Inffected
0	Heart Patient	8423	12346
1	Not Heart Patient	380350	644380

How many Heart disease people died after covid inffection

died_patient=pd.crosstab(index=df['CARDIOVASCULAR'], columns=df['DEAD'])
died_patient.reset_index(inplace=True)
died_patient

DEAD	CARDIOVASCULAR	Died	Not Died
0	Heart Patient	4435	16334
1	Not Heart Patient	71774	952956

Count of patience with heart disease got inffection and died

patients=pd.merge(inff_patient, died_patient, on='CARDIOVASCULAR', how='inner')
patients

	CARDIOVASCULAR	Infected	Non-Inffected	Died	Not Died
0	Heart Patient	8423	12346	4435	16334
1	Not Heart Patient	380350	644380	71774	952956

fig=px.bar(data_frame=patients, x='CARDIOVASCULAR', y=['Infected','Died'], text_auto=True,
          title="Count of patience with heart disease got inffection and died" )
fig.show()

50% Cardiovescular related dieases people died after covid inffection.

How many people with Obesity got covid inffected

inff_patient=pd.crosstab(index=df['OBESITY'], columns=df['CLASIFFICATION_FINAL'])
inff_patient.reset_index(inplace=True)
inff_patient

CLASIFFICATION_FINAL	OBESITY	Infected	Non-Inffected
0	Not Obese	316391	569336
1	Obese	72413	87403

How many Obese people died

died_patient=pd.crosstab(index=df['OBESITY'], columns=df['DEAD'])
died_patient.reset_index(inplace=True)
died_patient

DEAD	OBESITY	Died	Not Died
0	Not Obese	58933	826794
1	Obese	17294	142522

patients=pd.merge(inff_patient, died_patient, on='OBESITY', how='inner')
patients

	OBESITY	Infected	Non-Inffected	Died	Not Died
0	Not Obese	316391	569336	58933	826794
1	Obese	72413	87403	17294	142522

fig=px.bar(data_frame=patients, x='OBESITY', y=['Infected','Died'], text_auto=True,
          title="Count of Hypertensed people died after corona inffection" )
fig.show()

About 20% People with Obesity died due to covid inffection.

How many people with Chronical Renel disease had covid inffection

inff_patient=pd.crosstab(index=df['RENAL_CHRONIC'], columns=df['CLASIFFICATION_FINAL'])
inff_patient.reset_index(inplace=True)
inff_patient

CLASIFFICATION_FINAL	RENAL_CHRONIC	Infected	Non-Inffected
0	Chronic	7820	11084
1	Not Chronic	380994	645671

How many people with Chronical Renel disease has died

died_patient=pd.crosstab(index=df['RENAL_CHRONIC'], columns=df['DEAD'])
died_patient.reset_index(inplace=True)
died_patient

DEAD	RENAL_CHRONIC	Died	Not Died
0	Chronic	5707	13197
1	Not Chronic	70532	956133

patients=pd.merge(inff_patient, died_patient, on='RENAL_CHRONIC', how='inner')
patients

	RENAL_CHRONIC	Infected	Non-Inffected	Died	Not Died
0	Chronic	7820	11084	5707	13197
1	Not Chronic	380994	645671	70532	956133

fig=px.bar(data_frame=patients, x='RENAL_CHRONIC', y=['Infected','Died'], text_auto=True,
          title="Count of Chronic Renel Disease people died after corona inffection" )
fig.show()

75% Chronical diseases People died after corona virus inffection.

How many TOBACCO consumming people got corona inffection

inff_patient=pd.crosstab(index=df['TOBACCO'], columns=df['CLASIFFICATION_FINAL'])
inff_patient.reset_index(inplace=True)
inff_patient

CLASIFFICATION_FINAL	TOBACCO	Infected	Non-Inffected
0	Consume Tobacco	28624	55752
1	Not Consume Tobacco	360106	600873

How many TOBACCO consumming people died after inffection

died_patient=pd.crosstab(index=df['TOBACCO'], columns=df['DEAD'])
died_patient.reset_index(inplace=True)
died_patient

DEAD	TOBACCO	Died	Not Died
0	Consume Tobacco	6596	77780
1	Not Consume Tobacco	69621	891358

patients=pd.merge(inff_patient, died_patient, on='TOBACCO', how='inner')
patients

	TOBACCO	Infected	Non-Inffected	Died	Not Died
0	Consume Tobacco	28624	55752	6596	77780
1	Not Consume Tobacco	360106	600873	69621	891358

fig=px.bar(data_frame=patients, x='TOBACCO', y=['Infected','Died'], text_auto=True,
          title="Count of Hypertensed people died after corona inffection" )
fig.show()

Only 2% Tobacco consuming people died after corona virus inffection.

How Many died in ICU

died_patient=pd.crosstab(index=df['ICU'], columns=df['DEAD'])
died_patient.reset_index(inplace=True)
died_patient

DEAD	ICU	Died	Not Died
0	In ICU	8195	8663
1	Not in ICU	59775	115910

fig=px.bar(data_frame=died_patient, x='ICU', y=['Not Died','Died'], text_auto=True,
          title="Patients died in ICU" )
fig.show()

About 99% Patients who admitted to ICU are died. Most number of Patients died outside the ICU.

Deaths by Moths

df['Date']=df['DATE_DIED'].dt.to_period('M')

deaths=pd.crosstab(index=df['Date'], columns=df['DEAD'])
deaths.reset_index(inplace=True)
deaths

DEAD	Date	Died
0	2020-01	2738
1	2020-02	2613
2	2020-03	2819
3	2020-04	8192
4	2020-05	16621
5	2020-06	17888
6	2020-07	12401
7	2020-08	2850
8	2020-09	2631
9	2020-10	2609
10	2020-11	2552
11	2020-12	2711
12	2021-01	55
13	2021-02	43
14	2021-03	34
15	2021-04	156
16	2021-05	2
17	2021-06	5
18	2021-07	3
19	2021-08	4
20	2021-09	2
21	2021-10	5
22	2021-11	3
23	2021-12	5

Summary/Conclusion:

1. Any age People are getting inffected by the Corona Virus.
2. More People died after taking the primary treatment as compared to the people who has not taken any primary treatment and More Patients died in Medical Unit=4 then in Medical Unit=12.
3. Male and Female both are getting Inffected with same rate but the Male Deaths are more than the Female.
4. Most of the Hospitalized people died.
5. Many Patients died without availability of Ventilators. And when the patient went on Ventilators the chances of death is high.
6. About 70% Corona Inffected Pneumonia Patients died.
7. Less amount Pregnent womens got inffected and the death count is also very low.
8. About 40% Diabetic Patients died after getting inffected by Corona VIrus.
9. Death rate for COP disease people after getting covid inffection is very high, almost 70% Patients died.
10. 10% Asthma Patient died after covid inffection.
11. 55% Low immune system people died afted covid affection. Very less percentage of people with strong Immune System died after getting infected.
12. About 40% Inffected Hypertension people died.
13. 50% Cardiovescular related dieases people died after covid inffection.
14. About 20% People with Obesity, died due to covid inffection.
15. 75% Chronical diseases People died after corona virus inffection.
16. Only 2% Tobacco consuming people died after corona virus inffection.
17. About 99% Patients who admitted to ICU are died. Most number of Patients died outside the ICU.
18. In May, June and July 2020 Covid on its peak hence more Deaths recorded in these 3 Months.